医学文献摘要综述:从机器学习技术到大型语言模型

Akash Ghosh, Raghav Jain, Anubhav Jhangra, Sriparna Saha, Adam Jatowt
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引用次数: 0

摘要

互联网的广泛采用使医疗文档的数字化存储、共享和管理成为可能,从而改变了医疗保健行业。这一转变改善了信息获取,加强了患者护理,并为研究和创新开辟了新的途径。随着临床医生和患者可用的医疗数据量不断增长,对有效汇总方法的需求变得越来越重要。最近深度学习的突破——特别是大型语言模型(llm)的出现——进一步加速了这一领域的进展。本文全面综述了医学文献摘要的最新技术和发展趋势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models

A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models
The widespread adoption of the Internet has transformed healthcare by enabling the digital storage, sharing, and management of medical documents. This shift has improved information access, enhanced patient care, and opened new avenues for research and innovation. As the volume of medical data available to clinicians and patients continues to grow, the need for effective summarization methods becomes increasingly critical. Recent breakthroughs in deep learning—particularly the emergence of Large Language Models (LLMs)—have further accelerated progress in this area. This paper provides a comprehensive survey of current techniques and emerging trends in medical document summarization.
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